COVID-19 Progression
Last Updated: 02-July-2020 19:06 (UTC+9:30)
library(tidyverse)
library(magrittr)
library(lubridate)
library(scales)
library(matrixStats)
library(ggrepel)
library(broom)
library(glue)
library(jsonlite)
library(rvest)
library(RCurl)
library(pander)
library(DT)
library(plotly)
library(cowplot)
library(QuantTools)
library(ggfortify)
library(readxl)
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
panderOptions("missing", "")
theme_set(theme_bw())shiftAxisLabel <- function(x, k = 2){
x$x$layout$annotations[[2]]$x <- x$x$layout$annotations[[2]]$x*k
x$x$layout$margin$l <- x$x$layout$margin$l*k
x
}# Handle updates between 12am & 9am
dt <- Sys.Date()
if (as.numeric(format(Sys.time(), "%H")) <= 9){
dt <- Sys.Date() - 1
}auStates <- c(
ACT = "Australian Capital Territory",
QLD = "Queensland",
NSW = "New South Wales",
VIC = "Victoria",
SA = "South Australia",
WA = "Western Australia",
NT = "Northern Territory",
TAS = "Tasmania"
)Disclaimer: This very simple report was prepared by a bioinformatician with no experience in epidemiology or virology, and as such should be treated simply as an alternate viewpoint on the data, which I was simply unable to find elsewhere. Many other people exist with much greater expertise on this subject. However, I do hope this provides a useful perspective which is able to add constructively to the wider discussion. In addition, it should be noted that this is very much focussed on Australian data.
Data Sources
confirmed <- url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv") %>%
read_csv() %>%
pivot_longer(
cols = ends_with("20"),
names_to = "date",
values_to = "confirmed"
) %>%
mutate(
date = str_replace_all(
date, "(.+)/(.+)/(.+)", "20\\3-\\1-\\2"
) %>%
ymd()
) %>%
dplyr::rename(
Country = `Country/Region`
) %>%
dplyr::mutate(
Country = case_when(
`Province/State` == "Hubei" ~ "China (Hubei)",
`Province/State` == "Hong Kong" ~ "Hong Kong",
grepl("China", Country) & !`Province/State` %in% c("Hubei", "Hong Kong") ~ "China (Other)",
Country == "Korea, South" ~ "South Korea",
Country == "Congo (Kinshasa)" ~ "DR Congo",
Country == "Congo (Brazzaville)" ~ "Congo",
!grepl("China", Country) ~ Country
)
) %>%
dplyr::filter(
!is.na(confirmed)
) %>%
dplyr::select(-Lat, -Long)guardData <- fromJSON("https://interactive.guim.co.uk/docsdata/1q5gdePANXci8enuiS4oHUJxcxC13d6bjMRSicakychE.json")
altAU <- guardData$sheets$updates %>%
as_tibble() %>%
mutate(
`Province/State` = auStates[State],
Country = "Australia",
Date = parse_date_time(Date, orders = "%d/%m/%Y") %>% ymd()
) %>%
dplyr::select(`Province/State`, Country, date = Date, confirmed = `Cumulative case count`) %>%
mutate(confirmed = as.numeric(confirmed)) %>%
arrange(`Province/State`, date) %>%
tidyr::complete(`Province/State`, Country, date) %>%
group_by(`Province/State`) %>%
fill(confirmed) %>%
dplyr::filter(!is.na(confirmed)) %>%
ungroup()
# Add the data, with the Guardian API being preferentialy selected
confirmed %<>%
mutate(source = "JHU") %>%
bind_rows(
mutate(altAU, source = "API")
) %>%
arrange(
date, `Province/State`, source
) %>%
distinct(`Province/State`, Country, date, .keep_all = TRUE) %>%
dplyr::select(-source)latestAU <- list()nswRecov <- nswTests <- NA_real_
nswUrl <- "https://www.health.nsw.gov.au/_layouts/feed.aspx?xsl=1&web=/news&page=4ac47e14-04a9-4016-b501-65a23280e841&wp=baabf81e-a904-44f1-8d59-5f6d56519965&pageurl=/news/Pages/rss-nsw-health.aspx" %>%
read_xml() %>%
xml_find_all("//item") %>%
.[[1]] %>%
as.character() %>%
str_split("\n") %>%
.[[1]] %>%
str_subset("https") %>%
str_extract("https[^<]+")
nswTable <- nswUrl %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all("//table") %>%
.[[1]] %>%
html_table() %>%
set_colnames(
str_replace_all(colnames(.), "\\u200b", "")
) %>%
mutate_all(str_replace_all, pattern = "\\u200b", replacement = "") %>%
mutate(
Count = str_remove_all(Count, "[\\*,]") %>% as.numeric,
Cases = str_replace_all(Cases, " +", " ")
)
nswTests <- nswTable %>%
dplyr::filter(
str_detect(Cases, "Total tests carried out")
) %>%
.[["Count"]]
nswRecov <- nswTable %>%
dplyr::filter(str_detect(Cases, "recovered")) %>%
.[["Count"]]
latestAU$NSW <- tibble(
State = "New South Wales",
date = dt,
confirmed = nswTable %>%
dplyr::filter(str_detect(Cases, "Confirmed cases")) %>%
.[["Count"]],
deaths = nswTable %>%
dplyr::filter(str_detect(Cases, "Deaths")) %>%
.[["Count"]],
recovered = nswRecov,
tested = nswTests
)qldRecov <- qldTests <- NA_real_
qldUrl <- "https://www.qld.gov.au/health/conditions/health-alerts/coronavirus-covid-19/current-status/statistics"
qldTests <- qldUrl %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all("//div[contains(@class, 'tested')]") %>%
html_text() %>%
str_extract("[0-9,]+") %>%
str_remove_all(",") %>%
as.numeric()
qldTable <- qldUrl %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all("//table[@id = 'QLD_Cases']") %>%
html_table() %>%
.[[1]]
qldConfirmed <- qldTable %>%
dplyr::filter(str_detect(Cases, "Number of cases")) %>%
pull(Total) %>%
str_remove_all(",") %>%
as.numeric()
qldActive <- qldTable %>%
dplyr::filter(str_detect(Cases, "Active")) %>%
pull(Total) %>%
as.numeric()
qldDeaths <- qldTable %>%
dplyr::filter(str_detect(Cases, "Deaths")) %>%
pull(Total) %>%
as.numeric()
# qldRecov <- qldTable %>%
# dplyr::filter(Cases == "Recovered") %>%
# .[["Total"]] %>%
# str_remove_all(",") %>%
# as.numeric()
qldRecov <- qldConfirmed - qldActive - qldDeaths
latestAU$QLD <- tibble(
State = "Queensland",
date = dt,
confirmed = qldConfirmed,
deaths = qldDeaths,
recovered = qldRecov,
tested = qldTests
)vicRecov <- vicTests <- NA_real_
vicUrl <- c(
"https://www.dhhs.vic.gov.au/coronavirus-update-victoria-{format(dt, '%d')}-{format.Date(dt, '%B')}-{year(dt)}",
"https://www.dhhs.vic.gov.au/coronavirus-update-victoria-{day(dt)}-{format.Date(dt, '%B')}-{year(dt)}",
"https://www.dhhs.vic.gov.au/coronavirus-update-victoria-{format(dt - 1, '%d')}-{format.Date(dt - 1, '%B')}-{year(dt-1)}",
"https://www.dhhs.vic.gov.au/coronavirus-update-victoria-{day(dt - 1)}-{format.Date(dt - 1, '%B')}-{year(dt-1)}"
) %>%
vapply(glue, character(1)) %>%
str_to_lower() %>%
vapply(url.exists, logical(1)) %>%
which() %>%
names() %>%
.[[1]]
vicTxt <- vicUrl %>%
read_html() %>%
html_nodes("body") %>%
html_text() %>%
str_split("\n") %>%
.[[1]] %>%
str_trim() %>%
.[. != ""] %>%
str_subset("(test|recovered|total|died)") %>%
stringi::stri_trans_general("latin-ascii")
vicTests <- vicTxt %>%
str_subset(".*[0-9]+.*tests") %>%
.[[1]] %>%
str_replace_all(
".* ([0-9,]+)[^0-9]test(ed|s|ing).+",
"\\1"
) %>%
str_remove_all(",") %>%
as.numeric()
vicRecov <- vicTxt %>%
str_subset("[0-9]+.+recovered") %>%
str_trim() %>%
str_replace_all(".*[^0-9,]([0-9,]+)[^0-9]*people have recovered.*", "\\1") %>%
str_remove(",") %>%
as.numeric()
latestAU$VIC <- tibble(
State = "Victoria",
date = dt,
confirmed = vicTxt %>%
str_subset("(coronavirus|total).+cases") %>%
str_subset("Victoria") %>%
# str_subset("regional", negate = TRUE) %>%
# str_replace_all(".+[^0-9,]+ ([0-9,]+) cases[^0-9,].+", "\\1") %>%
str_replace_all(".+total ([0-9,]+) cases.+", "\\1") %>%
str_remove_all(",") %>%
as.numeric() %>%
.[!is.na(.)] %>%
unique(),
deaths = vicTxt %>%
str_subset("died") %>%
str_replace_all(".+[^0-9]([0-9,]+)[^0-9]*people have died.+", "\\1") %>%
str_remove_all(",") %>%
as.numeric() %>%
unique(),
recovered = vicRecov,
tested = vicTests
)waTests <- waRecov <- NA_real_
waMedia <- "https://ww2.health.wa.gov.au/News/Media-releases-listing-page" %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all("//a[contains(@title, '19 update')]") %>%
html_attr("href") %>%
str_subset("2020$") %>%
.[[1]]
waUrl <- glue("https://ww2.health.wa.gov.au{waMedia}")
waTxt <- waUrl %>%
read_html() %>%
html_nodes("body") %>%
html_text() %>%
str_split("\n") %>%
.[[1]] %>%
str_trim() %>%
.[. != ""] %>%
stringi::stri_trans_general("latin-ascii")
waTests <- waTxt %>%
str_subset("test(s|ed).*performed") %>%
str_replace_all(".+ ([0-9,]+) COVID.+", "\\1") %>%
str_remove_all(",") %>%
as.numeric()
waRecov <- waTxt %>%
str_subset("recovered") %>%
str_subset("[0-9]..") %>%
str_remove_all("COVID-19") %>%
str_replace_all("[^0-9]*([0-9]*)[^0-9]*", "\\1") %>%
as.numeric()
latestAU$WA <- tibble(
State = "Western Australia",
date = dt,
confirmed = waTxt %>%
str_subset("(State|Western)") %>%
str_subset("total") %>%
str_subset("recovered", TRUE) %>%
str_remove_all("COVID.*19") %>%
str_replace_all("[^0-9]+([0-9]+)[^0-9]+", "\\1") %>%
as.numeric() %>%
.[!is.na(.)] %>%
unique(),
deaths = 9, # Needs to be done manually again
recovered = waRecov,
tested = waTests
)saRecov <- saTests <- NA_real_
saUrl <- "https://www.covid-19.sa.gov.au/home/dashboard"
saTxt <- saUrl %>%
read_html() %>%
xml_find_all("//div[contains(@class, 'accordion__content')]") %>%
.[[1]] %>%
html_text() %>%
str_split("\r\n") %>%
.[[1]] %>%
str_trim() %>%
.[. != ""]
## The dashboard is unreliable so use SA Health press releases as an additional source
## These don't appear until late afternoon, so this is a pain in the arse quite frankly
altSaUrl <- c(
"https://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/about+us/news+and+media/all+media+releases/covid-19+update+{day(dt)}+{format(dt, '%B')}",
"https://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/about+us/news+and+media/all+media+releases/covid-19+update+{day(dt - 1)}+{format(dt - 1, '%B')}",
"https://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/about+us/news+and+media/all+media+releases/covid-19+update+{day(dt)}+{format(dt, '%B')}+2020",
"https://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/about+us/news+and+media/all+media+releases/covid-19+update+{day(dt - 1)}+{format(dt - 1, '%B')}+2020"
) %>%
vapply(glue, character(1)) %>%
str_to_lower()
validSa <- altSaUrl %>%
vapply(url.exists, logical(1)) %>%
which() %>%
names() %>%
sapply(read_html, simplify = FALSE) %>%
lapply(html_node, css = "body") %>%
vapply(function(x){!is.na(x)}, logical(1)) %>%
which() %>%
names() %>%
.[[1]]
altSaTxt <- validSa %>%
read_html() %>%
html_node("body") %>%
xml_find_all("//section[@class = 'main-content']") %>%
html_text() %>%
str_split("\n") %>%
.[[1]] %>%
str_trim() %>%
.[. != ""]
saTests <- c(
saTxt %>%
str_replace_all(".+[^0-9,]([0-9,]+)[^0-9,]+tests.+", "\\1") %>%
str_remove_all("[, ]") %>%
as.numeric(),
altSaTxt %>%
str_subset("tests") %>%
str_replace_all(".+ ([0-9,]+) test.+", "\\1") %>%
str_remove_all(",") %>%
as.numeric()
) %>%
max(na.rm = TRUE)
saTable <- "https://www.covid-19.sa.gov.au/" %>%
read_html() %>%
html_node("body") %>%
xml_find_all("//div[@class = 'focus-boxes']") %>%
html_text() %>%
str_split("\r\n") %>%
.[[1]] %>%
str_trim() %>%
.[.!=""] %>%
.[seq(length(.), 1, by = -1)] %>%
.[!str_detect(., "(Data current|View the|See more)")] %>%
matrix(ncol = 2, byrow = TRUE) %>%
set_colnames(c("Cases", "Total")) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
mutate(
Total = as.numeric(Total),
Cases = str_remove_all(Cases, "in South Australia")
)
saRecov <- c(
saTable %>%
dplyr::filter(str_detect(Cases, "recovered")) %>%
.[["Total"]],
altSaTxt %>%
str_subset("(cleared|recovered)") %>%
str_replace_all(".+ ([0-9]+) people have.+", "\\1") %>%
as.numeric()
) %>%
max(na.rm = TRUE)
latestAU$SA <- tibble(
State = "South Australia",
date = dt,
confirmed = saTable %>%
dplyr::filter(str_detect(Cases, "Total cases")) %>%
.[["Total"]],
deaths = 4, # Only able to be added manually. Thanks SA Health
recovered = saRecov,
tested = saTests
)actRecov <- actTests <- NA_real_
actUrl <- "https://www.covid19.act.gov.au/home"
actTable <- actUrl %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all(
"//div[contains(@class, 'spf-article-card--tabular')]"
) %>%
html_text() %>%
str_split("\r\n") %>%
.[[4]] %>%
str_trim() %>%
setdiff(y = "") %>%
str_remove_all("[,*]") %>%
matrix(ncol = 2, byrow = TRUE) %>%
set_colnames(c("Cases", "Total")) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
mutate(Total = as.numeric(Total))
actTests <- actTable %>%
dplyr::filter(!str_detect(Cases, "recovered")) %>%
.[["Total"]] %>%
sum()
actRecov <- actTable %>%
dplyr::filter(str_detect(Cases, "recov")) %>%
.[["Total"]]
latestAU$ACT <- tibble(
State = "Australian Capital Territory",
date = dt,
confirmed = actTable %>%
dplyr::filter(str_detect(Cases, "Confirmed cases")) %>%
.[["Total"]],
deaths = 3, # Only able to be added manually.
recovered = actRecov,
tested = actTests
)tasUrl <- "https://www.coronavirus.tas.gov.au/facts/cases-and-testing-updates"
tasTables <- tasUrl %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all("//table") %>%
html_table() %>%
.[1:2] %>%
lapply(mutate, Number = str_remove_all(Number, "[^0-9]")) %>%
lapply(mutate, Number = as.numeric(Number))
latestAU$TAS <- tibble(
State = "Tasmania",
date = dt,
confirmed = dplyr::filter(
tasTables[[2]],
str_detect(`Cases in Tasmania`, "Total cases")
)$Number,
deaths = dplyr::filter(
tasTables[[2]],
str_detect(`Cases in Tasmania`, "Deaths")
)$Number,
recovered = dplyr::filter(
tasTables[[2]],
`Cases in Tasmania` == "Recovered"
)$Number,
tested = dplyr::filter(
tasTables[[1]],
`Laboratory tests` == "Total laboratory tests"
)$Number
)# ntRecov <- ntTests <- NA_real_
# ntUrl <- "https://coronavirus.nt.gov.au/"
# ntTxt <- ntUrl %>%
# read_html() %>%
# html_nodes("body") %>%
# xml_find_all(
# "//div[contains(@class, 'header-widget')]"
# ) %>%
# html_text() %>%
# str_split("\r\n") %>%
# .[[1]] %>%
# str_trim() %>%
# .[. != ""]
# ntTests <- ntTxt %>%
# str_subset("test") %>%
# str_extract("([0-9,]+)") %>%
# str_remove_all(",") %>%
# as.numeric()
# ntRecov <- ntTxt %>%
# str_subset("recovered") %>%
# str_extract("[0-9,]+") %>%
# str_remove_all(",") %>%
# as.numeric()
ntUrl <- "https://coronavirus.nt.gov.au/current-status"
ntTable <- ntUrl %>%
read_html() %>%
html_nodes("body") %>%
xml_find_all("//div[contains(@class, 'covid-card-stats')]") %>%
html_text() %>%
str_split("\r\n") %>%
.[[1]] %>%
str_trim() %>%
.[. != ""] %>%
matrix(ncol = 2, byrow = TRUE) %>%
set_colnames(c("Total", "Category")) %>%
as.data.frame() %>%
mutate(Total = str_remove_all(Total, ",") %>% as.numeric())
latestAU$NT <- tibble(
State = "Northern Territory",
date = dt,
confirmed = dplyr::filter(ntTable, str_detect(Category, "confirmed"))$Total,
deaths = 0,
recovered = dplyr::filter(ntTable, str_detect(Category, "recovered"))$Total,
tested = dplyr::filter(ntTable, str_detect(Category, "tests"))$Total
)latestAU %<>%
bind_rows() %>%
mutate(Country = "Australia")latestAU %>%
dplyr::select(
`Province/State` = State, Country, date, recovered
) %>%
write_tsv(
here::here(glue("recovered/recovered_{dt}.tsv"))
)confirmed %<>%
bind_rows(
dplyr::select(latestAU, any_of(colnames(.)), `Province/State` = State)
) %>%
arrange(date) %>%
mutate(f = paste(`Province/State`, Country, sep = "_")) %>%
split(f = .$f) %>%
lapply(mutate, confirmed = cummax(confirmed)) %>%
bind_rows() %>%
dplyr::select(-f) %>%
arrange(Country, `Province/State`, date, desc(confirmed)) %>%
dplyr::distinct(`Province/State`, Country, date, .keep_all = TRUE)# This is 24hrs behind but has the censored values
# We can update the recovered time series from here
# These deaths are more accurate than JHU and the JHU TS should be updated
nswJSON <- "https://nswdac-covid-19-postcode-heatmap.azurewebsites.net/datafiles/data_Cases2.json" %>%
fromJSON() %>%
.[["data"]] %>%
as_tibble() %>%
separate(Date, into = c("Day", "Month")) %>%
mutate(
Date = paste0("2020-", Month, "-", Day) %>%
as_datetime(format = "%Y-%b-%d") %>%
ymd()
) %>%
group_by(Date) %>%
summarise(
confirmed = sum(Cases),
recovered = sum(Recovered + censored),
deaths = sum(Deaths)
) %>%
mutate(
`Province/State` = "New South Wales",
Country = "Australia"
) %>%
dplyr::rename(date = Date)auRecTS <- list.files(here::here("recovered"), pattern = "rec.*tsv",full.names = TRUE) %>%
lapply(read_tsv) %>%
bind_rows() %>%
dplyr::filter(!(date %in% nswJSON$date & `Province/State` == "New South Wales")) %>%
bind_rows(dplyr::select(nswJSON, any_of(colnames(.)))) %>%
arrange(`Province/State`, date) %>%
group_by(`Province/State`) %>%
mutate(recovered = cummax(recovered))recovered <- url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv") %>%
read_csv() %>%
pivot_longer(
cols = ends_with("20"),
names_to = "date",
values_to = "recovered"
) %>%
mutate(
date = str_replace_all(
date, "(.+)/(.+)/(.+)", "20\\3-\\1-\\2"
) %>%
ymd()
) %>%
dplyr::rename(
Country = `Country/Region`
) %>%
dplyr::mutate(
Country = case_when(
`Province/State` == "Hubei" ~ "China (Hubei)",
`Province/State` == "Hong Kong" ~ "Hong Kong",
grepl("China", Country) & !`Province/State` %in% c("Hubei", "Hong Kong") ~ "China (Other)",
grepl("Korea, South", Country) ~ "South Korea",
Country == "Congo (Kinshasa)" ~ "DR Congo",
Country == "Congo (Brazzaville)" ~ "Congo",
!grepl("China", Country) ~ Country
)
) %>%
dplyr::select(-Lat, -Long) %>%
bind_rows(auRecTS) %>%
group_by(
`Province/State`, Country, date
) %>%
summarise_at(vars(recovered), max)deaths <- url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv") %>%
read_csv() %>%
pivot_longer(
cols = ends_with("20"),
names_to = "date",
values_to = "deaths"
) %>%
mutate(
date = str_replace_all(
date, "(.+)/(.+)/(.+)", "20\\3-\\1-\\2"
) %>%
ymd()
) %>%
dplyr::rename(
Country = `Country/Region`
) %>%
dplyr::mutate(
Country = case_when(
`Province/State` == "Hubei" ~ "China (Hubei)",
`Province/State` == "Hong Kong" ~ "Hong Kong",
grepl("China", Country) & !`Province/State` %in% c("Hubei", "Hong Kong") ~ "China (Other)",
Country == "Korea, South" ~ "South Korea",
Country == "Congo (Kinshasa)" ~ "DR Congo",
Country == "Congo (Brazzaville)" ~ "Congo",
!grepl("China", Country) ~ Country
)
) %>%
dplyr::select(-Lat, -Long) %>%
bind_rows(
dplyr::select(latestAU, any_of(colnames(.)), `Province/State` = State)
) %>%
bind_rows(
dplyr::select(nswJSON, any_of(colnames(.)))
) %>%
arrange(date) %>%
mutate(f = paste(`Province/State`, Country, sep = "_")) %>%
split(f = .$f) %>%
lapply(mutate, deaths = cummax(deaths)) %>%
bind_rows() %>%
dplyr::select(-f) %>%
dplyr::distinct(`Province/State`, Country, date, .keep_all = TRUE) predRecovered <- confirmed %>%
rename(
recovered = confirmed
) %>%
mutate(
date = date + 21
) %>%
left_join(deaths) %>%
mutate(recovered = recovered - deaths) %>%
dplyr::select(
`Province/State`, Country, date,recovered
)
# predRecovered %>%
# rename(pred = recovered) %>%
# left_join(recovered) %>%
# group_by(Country, date) %>%
# summarise_at(
# vars(pred, recovered), sum
# ) %>%
# ungroup() %>%
# dplyr::filter(Country != "Sweden") %>%
# ggplot(aes(pred, recovered)) +
# geom_point() +
# geom_abline(slope = 1) +
# geom_smooth(method = "lm") +
# scale_x_log10(labels = comma) +
# scale_y_log10(labels = comma)wikiPops <- read_tsv("wikiPops.tsv") %>%
mutate_at(c("Region", "Continent"), as.factor)alwaysShow <- c("Australia", "New Zealand", "US", "Canada", "United Kingdom", "Taiwan*", "Singapore", "South Korea", "China (Other)", "China (Hubei)", "Hong Kong", "Japan", "Ireland", "Russia", "Brazil", "Kuwait", "Finland", "Norway")countryCols <- confirmed %>%
dplyr::filter(confirmed > 0) %>%
arrange(date) %>%
distinct(Country) %>%
left_join(wikiPops) %>%
mutate(
Region = str_replace_all(
Region, "(Southern|Western) Europe", "Southern & Western Europe"
),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
)
) %>%
droplevels() %>%
split(f = .$Region) %>%
lapply(
function(x){
mutate(x, rgb = hue_pal()(nrow(x)))
}
) %>%
bind_rows() %>%
with(structure(rgb, names = Country)) Data for confirmed cases, recoveries and fatalities was primarily sourced from Johns Hopkins University, using the datasets provided at https://github.com/CSSEGISandData/COVID-19. JHU data is now updated every 24 hours at approximately 3:30(UTC), which is about 1:00PM in Adelaide. As such no accurate, daily updates for international data can be produced until after that time. Importantly, dates associated with confirmed cases from this data source, may differ from dates associated with confirmed cases from Australian sources. For example, cases reported in the morning in Australia may be assigned to the previous day in US data sources.
Live hourly updates for Australia are available from https://covid-19-au.github.io/ for those who would like an up to the minute breakdown of confirmed cases. Numbers used for generation of this page are updated periodically throughout the day using values provided by individual states at NSW, QLD, VIC, WA, SA, TAS, ACT and the NT. Additional Australian data was obtained from The Guardian. Whilst dates from the Guardian are likely to be reported using the UK as a reference, this is still closer to Australian time zones than USA-based data.
Population sizes were obtained from 2019 UN Estimates. Given the disparity of infection within China, China was broken into Hubei Province and the rest of China, with Hong Kong and Taiwan already being considered as separate countries in all datasets. Population estimates for Hubei Province were taken from the 2018 estimates given by Statista.com and this is likely to be a very slight underestimate.
Confirmed cases of COVID-19 as provided by the Chinese Government have been discussed elsewhere as unusual, and data appears potentially unreliable. In this analysis, discussions regarding accurate Chinese reporting are not considered further and data is simply presented as supplied by JHU.
However, all countries are likely to contain many unreported cases given the incomplete testing regimes in place for most countries. Similarly, reporting in many countries may have features that cause concerns regarding data integrity and this makes comparison across countries difficult. Information on recovered cases has been difficult to accurately obtain due inconsistent methods for considering a case as recovered, and lack of reporting for these cases in many jurisdictions. In Australia, some states (e.g. NSW & QLD) have only begun releasing these figures in mid-April, whilst other states such as Victoria were releasing these numbers from mid-March.
International Data
startingPoint <- 4
minPop <- 2e6For this section, most data is presented relative to population size. Growth in infection rates is only shown after the point at which the cumulative confirmed infection rate breached 4 confirmed cases / million. This equates to about 101 confirmed cases within Australia, and is broadly comparable to the “Days since passing 100 confirmed cases” commonly shown elsewhere.
Most plots in this section have been filtered for countries with a population, cases or fatalities larger than a given value. Due to their significantly strong or poor performances, a handful of countries have been specified to be always included regardless of this filter. These countries are Australia, Brazil, Canada, China (Hubei), China (Other), Finland, Hong Kong, Ireland, Japan, Kuwait, New Zealand, Norway, Russia, Singapore, South Korea, Taiwan, US and United Kingdom
- At the time of report preparation, the total number of confirmed cases, including all countries for which data has been made available, is 10,694,071.
- The time period for the most recent doubling of global cases was 39 days.
- The last million cases were confirmed over the last 6 days.
Regional Data
Cumulative Infection Rates
Before exploring individual countries, a regional perspective may be helpful. Importantly, only Oceania, Eastern Asia, Western Europe and Southern Europe have successfully controlled the infection rate. Most other regions are still experiencing exponential growth.
ggplotly(
confirmed %>%
inner_join(wikiPops) %>%
mutate(
Continent = str_replace_all(Continent, "(Oceania|Asia)", "Asia & Oceania")
) %>%
group_by(Continent, Region, Country, Population, date) %>%
summarise(confirmed = sum(confirmed)) %>%
ungroup() %>%
group_by(Continent, Region, date) %>%
summarise(rate = round(1e6*sum(confirmed)/sum(Population), 2)) %>%
dplyr::filter(
date < dt,
date > "2020-02-18",
rate > startingPoint
# Region != "Eastern Asia"
) %>%
ggplot(aes(date, rate, colour = Region)) +
geom_line() +
scale_y_log10(label = comma_format(1)) +
facet_wrap(~Continent, ncol = 2) +
labs(x = "Date", y = "Cumulative Infection Rate (Confirmed Cases / Million)")
) %>%
shiftAxisLabel(k = 1.5)Current regional cumulative infection rates. Lines are shown from the point that regional cases exceeded 4 confirmed cases / million. Rates are calculated within each region using regional populations.
Daily New Infection Rates
ggplotly(
confirmed %>%
inner_join(wikiPops) %>%
mutate(
Continent = str_replace_all(Continent, "(Oceania|Asia)", "Asia & Oceania")
) %>%
group_by(Continent, Region, Country, Population, date) %>%
summarise(confirmed = sum(confirmed)) %>%
ungroup() %>%
group_by(Continent, date) %>%
mutate(Population = sum(Population)) %>%
ungroup() %>%
group_by(Continent, Region, date) %>%
summarise(
confirmed = sum(confirmed),
Population = unique(Population)
) %>%
mutate(
daily = c(0, diff(confirmed))
) %>%
ungroup() %>%
dplyr::filter(
daily > 0 | confirmed > 0,
date < dt
) %>%
mutate(
`Daily Rate` = round(1e6 * daily / Population, 2)
) %>%
rename(Date = date) %>%
ggplot(
aes(Date, `Daily Rate`, fill = Region)
) +
geom_bar(stat = "identity") +
geom_line(
aes(Date, MA),
data = . %>%
group_by(Continent, Date) %>%
summarise(
`Daily Rate` = sum(`Daily Rate`)
) %>%
mutate(MA = sma(`Daily Rate`, 7)),
inherit.aes = FALSE
) +
scale_colour_manual(values = c(NA, "black")) +
scale_x_date(expand = expansion(c(0, 0.03))) +
scale_y_continuous(expand = expansion(c(0, 0.05))) +
facet_wrap(~Continent, ncol = 2, scales = "free") +
labs(
x = "Date",
y = "Daily New Infection Rate (cases / million)"
),
tooltip = c("Date", "Daily Rate", "Region")
) %>%
shiftAxisLabel(k = 3)Daily new confirmed infections, shown as cases / million. Values are calculated within the total population for each continent. The solid black line is the 7-day simple moving average calculated within each continent
Infection Rates
Table of Most Impacted Countries
Confirmed cases in this table are effectively the cumulative, confirmed incidence rate. Recovered patients and those who have passed away are still included in these numbers.
confirmed %>%
group_by(Country, date) %>%
summarise(confirmed = sum(confirmed)) %>%
ungroup() %>%
group_by(Country) %>%
dplyr::filter(
date == max(date),
) %>%
ungroup() %>%
inner_join(wikiPops) %>%
mutate(
rate = 1e6*confirmed / Population,
occurrence = Population / confirmed,
Population = round(Population, -3) / 1e6
) %>%
dplyr::filter(rate >= 1) %>%
arrange(desc(rate)) %>%
rename_at(vars(everything()), str_to_title) %>%
dplyr::select(
Continent, Region, Country,
Date, Confirmed,
`Population (millions)` = Population,
`Rate (Cases per Million)` = Rate,
Occurrence
) %>%
datatable(
options = list(
pageLength = 25,
autoWidth = TRUE,
searchCols = list(
NULL, NULL, NULL, NULL, NULL,
list(
search = glue(
'{minPop/1e6 + 0.001} ... {max(wikiPops$Population/1e6)}'
)
),
NULL
)
),
filter = 'top',
class = "stripe",
rownames = FALSE,
caption = paste(
"The most impacted countries studied here and shown as a proportion of total population.",
"The initial filter is set so that only countries with a population greater than", comma(minPop), "are shown.",
"All fields are searchable and sortable.",
"To filter numeric columns, either use the slider or enter the values in the form 'min ... max'.",
"To filter text columns, partial matching used in a case-insensitive manner.",
"Populations have been rounded to the nearest thousand to make reading values easier.",
"'Rate' represents the latest confirmed infection rate as cumulative cases per million people, whilst 'Occurrence' represents the number of people expected before one case is found, assuming an even distribution amongst the population.",
"In other words, one in every 'Occurrence' people within the population have been confirmed to have contracted COVID-19.",
"Occurrence is inversely proportional to Rate.",
"No adjustment has been made in this table for patients who have recovered or passed away.",
"Whilst the virus spreads with no regard to population size, the rate as shown here indicates the degree of stress which each country's health-care system is likely to be experiencing.",
"Several countries shown here have not attracted much media attention due lower case numbers than China and Italy, but are likely to be experiencing significant duress.",
"Continent and Region information is as provided by the UN classifications."
)
) %>%
formatCurrency(
columns = c("Population (millions)"),
currency = "",
digits = 3,
mark = ","
) %>%
formatCurrency(
columns = c("Confirmed", "Rate (Cases per Million)", "Occurrence"),
currency = "",
digits = 0,
mark = ","
)Cumulative Incidence Rates
ausDays <- confirmed %>%
dplyr::filter(Country == "Australia") %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed), sum) %>%
left_join(wikiPops) %>%
mutate(rate = 1e6 * confirmed / Population) %>%
dplyr::filter(rate > startingPoint) %>%
nrow()
minDays <- ausDays - 30
# Use Singapore as that has the longest dataset besides Hubei
nDays <- confirmed %>%
dplyr::filter(Country == "Singapore") %>%
group_by(Country, date) %>%
summarise(confirmed = sum(confirmed)) %>%
ungroup() %>%
left_join(wikiPops) %>%
mutate(
rate = 1e6*confirmed / Population
) %>%
dplyr::filter(rate > startingPoint) %>%
nrow() %>%
subtract(1)refRate <- c(2, 4, 8)
minPop <- 8e6
p <- confirmed %>%
group_by(Country, date) %>%
summarise(confirmed = sum(confirmed)) %>%
ungroup() %>%
inner_join(
dplyr::filter(
wikiPops, Population > minPop | Country %in% alwaysShow
)
) %>%
mutate(
rate = 1e6*confirmed / Population
) %>%
dplyr::filter(
rate > startingPoint
) %>%
group_by(Country) %>%
mutate(
days = date - min(date)
) %>%
dplyr::filter(
max(days) >= minDays | Country %in% alwaysShow
) %>%
ungroup() %>%
mutate(
days = as.integer(days),
rate = round(rate, 2)
) %>%
dplyr::filter(days <= nDays | Country %in% alwaysShow) %>%
arrange(date) %>%
mutate(
Region = str_replace_all(
Region, "(Southern|Western) Europe", "Southern & Western Europe"
),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
),
Country = fct_inorder(Country),
Region = fct_lump(Region, n = 11)
) %>%
rename_all(str_to_title) %>%
mutate(ymax = max(Rate)) %>%
ggplot(
aes(Days, Rate, colour = Country, Date = Date, Confirmed = Confirmed)
) +
geom_segment(
aes(x, y, xend = xmax, yend = ymax),
data = . %>%
dplyr::slice(seq_along(refRate)) %>%
dplyr::select(ymax) %>%
mutate(
ymax = ymax*1.2,
x = 0,
y = startingPoint,
xmax = refRate*log2(ymax / startingPoint)
),
inherit.aes = FALSE,
colour = "grey80",
linetype = 2
) +
geom_line() +
scale_x_continuous(
expand = expansion(mult = c(0, 0.05)),
) +
scale_y_log10(
expand = expansion(mult = c(0, 0.05)),
label = comma
) +
scale_colour_manual(values = countryCols) +
xlab(
paste(
"Days since passing",
startingPoint,
"confirmed cases/million"
)
) +
ylab("Confirmed Cumulative Infection Rate (cases/million)") +
facet_wrap(~Region, ncol = 3)ggplotly(
p,
tooltip = c(
"Days", "Rate", "Country", "Date", "Confirmed"
)) %>%
shiftAxisLabel(k = 1.5)COVID-19 Confirmed Cumulative Infection Rate for countries which have exceeded 4 confirmed cases/million for 85 or more days, and with populations greater than 8,000,000, apart from a small number of specifically included countries. Data is only shown for the first 148 calendar days since passing 4 confirmed cases/million. Note that from the day records begin in this dataset (2020-01-22), the confirmed infection rate in Hubei was 7.5 confirmed cases/million. Diagonal grey lines indicate a doubling in the infection rate every 2, 4, or 8 days. To hide a country, click on the country in the plot legend. Clicking again on the country in the legend will restore the data within the plot. Countries are shown in order of the date at which they passed the 4 confirmed case/million mark. Regions are as defined by the UN with some regions lumped together if only a few data points were available. Due to the large number of countries shown, you may need to scroll through the legend. Regions of the plot are also able to be zoomed interactively. Please note the y-axis is shown on the logarithmic scale, so that a series of points which appear to be diagonal will indicate exponential growth. The flatter the line, the slower the growth and a perfectly horizontal line would indicate zero growth, or no new confirmed cases.
Daily New Cases
minPop <- 5e6
minRate <- 12
ggplotly(
confirmed %>%
group_by(Country, date) %>%
summarise(confirmed = sum(confirmed)) %>%
mutate(
`daily total` = c(0, diff(confirmed)),
daily_ma = sma(`daily total`, 7)
) %>%
ungroup() %>%
dplyr::filter(`daily total` > 0 | confirmed > 0) %>%
inner_join(
dplyr::filter(
wikiPops, Population > minPop | Country %in% alwaysShow
)
) %>%
mutate(
`Daily Rate` = round(1e6 * daily_ma / Population, 2),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
# Region = str_replace_all(
# Region, "(Eastern|South-eastern) Asia", "Eastern & South-Eastern Asia"
# ),
Region = str_replace_all(
Region, "(Northern America|Caribbean)", "Caribbean & Northern America"
),
Region = str_replace_all(Region, "(.+) Africa", "Africa (All Regions)"),
Region = fct_lump(Region, n = 11)
) %>%
group_by(Country) %>%
dplyr::filter(
date > "2020-03-01",
date < dt,
max(`Daily Rate`) > minRate | Country %in% alwaysShow
) %>%
ungroup() %>%
rename_all(str_to_title) %>%
mutate(
`Population (millions)` = round(Population / 1e6, 2)
) %>%
ggplot(
aes(Date, `Daily Rate`, colour = Country, label = `Daily Total`, key = `Population (millions)`)
) +
geom_line() +
facet_wrap(
~Region, ncol = 3, scales = "free_y"
) +
labs(
y = "Daily New Confirmed Cases (per million)"
) +
scale_x_date(expand = expansion(c(0, 0.03))) +
scale_y_continuous(expand = expansion(c(0, 0.05))) +
scale_colour_manual(values = countryCols)
) %>%
shiftAxisLabel()Daily New Confirmed Cases (per million) using each country’s population to calculate the case rate. 7 day Simple Moving Averages are used to plot daily case rates in order to minimise the impact of single-day spikes and irregular reporting. Daily Totals provided when hovering are the reported number of cases for that specific day, without smoothing or scaling. Only countries with more than 4,000,000 people and where the daily rate exceeded 12 cases/million are shown.
Daily Increase Vs Confirmed Cases
An alternate viewpoint on the data is to remove time and inspect the relationship between the daily increase in cases and the total number of cases. When this relationship ceases it’s near linear relationship, this can be a sign the control measures have begun to take effect. Whilst this relationship appears to have broken down for Australia, no breakdown has yet occurred for countries such as South Africa, USA and Brazil, with these countries explicitly still in, or having resumed the exponential growth phase.
minPop <- 8e6
minDays <- 10
minRate <- 50
plotIncVConf <- confirmed %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed), sum) %>%
dplyr::filter(confirmed > 0) %>%
inner_join(
dplyr::filter(wikiPops, Population > minPop | Country %in% alwaysShow)
) %>%
mutate(
rate = 1e6 * confirmed / Population
) %>%
split(f = .$Country) %>%
lapply(function(x, n = 7){
x %>%
mutate(
d = c(0, diff(rate))
) %>%
mutate_at(
vars(d), sma, n = n
) %>%
dplyr::filter(
!is.na(d),
rate > minRate
) %>%
mutate(
nDays = nrow(.)
)
}
) %>%
bind_rows() %>%
ungroup() %>%
dplyr::filter(
nDays > minDays,
d > 0.01
) %>%
arrange(date) %>%
mutate(
Region = str_replace_all(
Region, "(Southern|Western) Europe", "Southern & Western Europe"
),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
),
Country = fct_inorder(Country),
rate = round(rate, 2),
d = round(d, 3),
Population = comma(round(Population, -3)),
Region = fct_lump(Region, n = 11)
) %>%
rename(
Rate = rate,
`Average Daily Increase` = d,
Date = date
) %>%
ggplot(
aes(Rate, `Average Daily Increase`, colour = Country, label = Population, key = Date)
) +
geom_line() +
scale_y_log10(
name = "Average Daily Increase (Cases / Million)",
label = label_comma(accuracy = 0.1)) +
scale_x_log10(
name = "Cumulative Confirmed Infection Rate (Cases / Million)"
) +
scale_colour_manual(values = countryCols) +
facet_wrap(~Region, ncol = 3) capIncVConf <- glue(
"*Daily Increase in Cases plotted against Confirmed Cases, using confirmed cases / million.
These two values are directly proportional until interventions are successful, at which point the proportional relationship changes, as evidenced by a sudden downwards turn.
Scaling by population aids in the visualisation of where in the relative infection trajectory each country's control measures have begun to take effect.
Daily increases are shown using a 7-day simple moving average in order to minimise the impact of day-to-day variation.
Countries are only shown from the point at which the moving average exceeds {minRate} cases/million, and have exceeded this value for > {minDays} days.
Regions are as defined by the UN with some combined for convenience if only a small number of countries were available.
Data is additionally restricted to countries with a population > {comma(minPop)}.*
"
)ggplotly(plotIncVConf +
coord_cartesian(
ylim = c(0.5, max(plotIncVConf$data$`Average Daily Increase`)))
) %>%
shiftAxisLabel(1.5)Daily Increase in Cases plotted against Confirmed Cases, using confirmed cases / million. These two values are directly proportional until interventions are successful, at which point the proportional relationship changes, as evidenced by a sudden downwards turn. Scaling by population aids in the visualisation of where in the relative infection trajectory each country’s control measures have begun to take effect. Daily increases are shown using a 7-day simple moving average in order to minimise the impact of day-to-day variation. Countries are only shown from the point at which the moving average exceeds 50 cases/million, and have exceeded this value for > 10 days. Regions are as defined by the UN with some combined for convenience if only a small number of countries were available. Data is additionally restricted to countries with a population > 8,000,000.
Comparison of Total Confirmed Infections
minPop <- 4e6
totalConf <- confirmed %>%
group_by(Country) %>%
dplyr::filter(date == max(date)) %>%
ungroup() %>%
summarise_at(vars(confirmed), sum) %>%
pull(confirmed)confirmed %>%
group_by(Country) %>%
dplyr::filter(date == max(date)) %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed), sum) %>%
ungroup() %>%
inner_join(wikiPops) %>%
dplyr::filter(Population > minPop) %>%
mutate(
`Infection Rate` = 1e6*confirmed / Population,
TotalRank = rank(1/confirmed),
RateRank = rank(1/`Infection Rate`),
AveRank = (TotalRank + RateRank) / 2
) %>%
arrange(desc(RateRank)) %>%
mutate(
Country = fct_inorder(Country)
) %>%
arrange(AveRank) %>%
dplyr::slice(1:25) %>%
droplevels() %>%
mutate(
Continent = fct_inorder(as.character(Continent))
) %>%
dplyr::select(-contains("Rank")) %>%
pivot_longer(
cols = c(confirmed, `Infection Rate`),
names_to = "name",
values_to = "value"
) %>%
mutate(
lab_y = case_when(
name == "confirmed" ~ value + 2.75e5,
name != "confirmed" ~ value + 1.2e3
)
) %>%
ggplot(aes(Country, value, fill = Country)) +
geom_col(colour = "black") +
geom_label(
aes(y = lab_y, label = comma_format(1)(value)),
alpha = 0.3
) +
facet_grid(
Continent~name,
scales = "free", space = "free_y",
labeller = as_labeller(
c(
`Infection Rate` = "Confirmed Cases / Million",
confirmed = "Total Confirmed Cases",
structure(levels(wikiPops$Continent), names = levels(wikiPops$Continent))
)
),
switch = "x"
) +
coord_flip() +
scale_fill_viridis_d(option = "magma") +
scale_y_continuous(labels = comma, expand = expansion(c(0, 0.1))) +
theme(
axis.title = element_blank(),
legend.position = "none",
panel.spacing.x = unit(0.02, "npc"),
strip.text = element_text(face = "bold"),
strip.placement = "outside"
)
Most impacted 25 countries when combining rankings across both total confirmed cases, and total confirmed cases/million. Only countries with a population greater than 4,000,000 are shown. At the time of preparation, the total number of global infections stands at 10,694,071.
Fatalities
fr <- confirmed %>%
inner_join(deaths) %>%
group_by(Country) %>%
dplyr::filter(date == max(date)) %>%
ungroup() %>%
summarise(fr = sum(deaths) / sum(confirmed)) %>%
.[["fr"]]The current fatality rate from all confirmed cases is 4.8%. This may be a function of under-reporting of true cases and is very likely to be an overestimate.
Table of Fatalities
minPop <- 4e6
minCases <- 1000
deaths %>%
left_join(confirmed) %>%
group_by(Country, date) %>%
summarise_at(vars(deaths, confirmed), sum) %>%
dplyr::filter(deaths > 0) %>%
left_join(wikiPops) %>%
ungroup() %>%
mutate(
infectionRate = round(1e6 * confirmed / Population, 1),
fatalityRate = deaths / confirmed,
fpm = round(1e6 * deaths / Population, 1),
Population = round(Population / 1e6, 3)
) %>%
arrange(desc(date), fatalityRate) %>%
distinct(Country, .keep_all = TRUE) %>%
arrange(desc(fpm)) %>%
dplyr::select(
Continent, Region, Country, Population,
`Confirmed Cases` = confirmed,
`Cases / Million` = infectionRate,
`Total Fatalities` = deaths,
`Fatalities / Million` = fpm,
`% Fatal Infections` = fatalityRate
) %>%
datatable(
options = list(
pageLength = 25,
autoWidth = TRUE,
searchCols = list(
NULL, NULL, NULL,
list(
search = glue(
'{minPop/1e6} ... {max(wikiPops$Population/1e6)}'
)
),
list(
search = glue(
'{minCases} ... {max(.$`Confirmed Cases`)}'
)
),
NULL, NULL, NULL
)
),
filter = 'top',
class = "stripe",
rownames = FALSE,
caption = glue(
"All countries with recorded fatalities, sorted by default in decreasing order of the Fatality Rate.
The Fatality Rate simply indicates the number of confirmed cases which end in a fatality.
The Infection Rate represents the cumulative number of cases confirmed within the country, per million people.
All columns are searchable and filters are set by default to exclude countries with populations below {comma(minPop)} and those with fewer than {minCases} confirmed cases."
)
) %>%
formatRound(
columns = "Cases / Million",
mark = ",",
digits = 1
) %>%
formatRound(
columns = "Total Fatalities",
mark = ",",
digits = 0
) %>%
formatPercentage(
columns = "% Fatal Infections",
digits = 2
) Cumulative Fatalities (Scaled)
minRate <- 2
minDays <- 24
minPop <- 5e6
scaledDeathPlot <- deaths %>%
group_by(Country, date) %>%
summarise_at(vars(deaths), sum) %>%
ungroup() %>%
dplyr::filter(deaths > 0) %>%
right_join(
wikiPops %>%
dplyr::filter(Population > minPop | Country %in% alwaysShow)
) %>%
mutate(
rate = round(1e6 * deaths / Population, 2)
) %>%
dplyr::filter(rate > minRate) %>%
group_by(Country) %>%
mutate(Days = as.integer(date - min(date))) %>%
dplyr::filter(
max(Days) > minDays | Country %in% alwaysShow
) %>%
arrange(desc(Days)) %>%
ungroup() %>%
mutate(
Region = str_replace_all(
Region, "(Southern|Western) Europe", "Southern & Western Europe"
),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
),
Country = fct_inorder(Country),
Region = fct_lump(Region, n = 11),
`One Death Every` = round(Population / deaths, 0),
deaths = comma(deaths),
`Population (millions)` = round(Population / 1e6, 1)
) %>%
rename_all(str_to_title) %>%
rename_all(str_replace_all, pattern = "Mill", replacement = "mill") %>%
ggplot(
aes(
x = Days, y = Rate, colour = Country, key = Date,
b = `Population (millions)`, label = Deaths,
family = `One Death Every`
)
) +
geom_line() +
scale_y_log10() +
scale_colour_manual(values = countryCols) +
labs(
x = glue("Days Since Passing {minRate} Deaths / Million"),
y = "Deaths / Million"
) +
facet_wrap(~Region, ncol = 3, scales = "free_y")scaledDeathPlot %>%
ggplotly(
tooltip = c("Country", "Date", "Rate", "Deaths", "One Death Every", "Population (millions)")
) %>%
shiftAxisLabel(1.5)Cumulative fatalities for all countries with a population greater than 5 million, who passed 2 deaths / million more than 24 days ago. In the legend at right, countries are shown in order of passing 2 deaths / million. Regions are shown as defined by the UN, however some with fewer points may have been merged for convenience.
Daily Fatalities
minPop <- 5e6
minRate <- 0.1
n <- 7ggplotly(
deaths %>%
group_by(Country, date) %>%
summarise_at(vars(deaths), sum) %>%
mutate(
daily = c(0, diff(deaths)),
MA = sma(daily, n)
) %>%
inner_join(wikiPops) %>%
ungroup() %>%
dplyr::filter(
Population > minPop | Country %in% alwaysShow,
!is.na(MA),
deaths > 0
) %>%
arrange(date) %>%
mutate(
rate = round(1e6 * MA / Population, 2),
Region = as.character(Region),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Eastern|South-eastern) Asia", "Eastern & South-Eastern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Western|Northern) Africa", "Western & Northern Africa"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
),
Region = fct_lump(Region, n = 11),
Country = fct_inorder(Country),
`Population (millions)` = round(Population / 1e6, 2)
) %>%
group_by(Country) %>%
dplyr::filter(max(rate) > minRate) %>%
dplyr::rename(
Date = date,
`Daily Fatility Rate` = rate,
`Actual Daily Total` = daily
) %>%
ggplot(
aes(Date, `Daily Fatility Rate`, colour = Country, label = `Population (millions)`, key = `Actual Daily Total`)
) +
geom_line() +
facet_wrap(~Region, ncol = 3, scales = "free_y") +
scale_colour_manual(values = countryCols) +
labs(
y = "Average Daily Fatalities (per million)"
)
) %>%
shiftAxisLabel(1.5)Average daily fatality rate (per million) using a 7-day moving average. Only countries with a population greater than 5,000,000 and a daily fatality rate that has exceeded 0.1 deaths/million are included. Clicking the Autoscale icon can be very helpful for this visualisation.
Daily Increase in Fatalities Vs Confirmed Fatalities
minPop <- 4e6
minDays <- 14
minRate <- 1
minD <- 0.45
dailyVsTotalDeaths <- deaths %>%
group_by(Country, date) %>%
summarise_at(vars(deaths), sum) %>%
dplyr::filter(deaths > 0) %>%
split(f = .$Country) %>%
lapply(function(x, n = 7){
x %>%
mutate(
d = c(0, diff(deaths))
) %>%
mutate_at(
vars(deaths, d), sma, n = n
)
}
) %>%
bind_rows() %>%
inner_join(wikiPops) %>%
mutate(
deaths = 1e6 * deaths / Population,
d = 1e6 * d / Population,
max_d = max(d, na.rm = TRUE)
) %>%
ungroup() %>%
dplyr::filter(!is.na(deaths), !is.na(d)) %>%
mutate_at(vars(deaths, d), round, 2) %>%
dplyr::filter(
Population > minPop | Country %in% alwaysShow,
max_d > minD | Country %in% alwaysShow,
deaths > minRate,
d > 0
) %>%
group_by(Country) %>%
mutate(n = n()) %>%
ungroup() %>%
dplyr::filter(n > minDays | Country %in% alwaysShow) %>%
rename_all(str_to_title) %>%
rename(
`Daily Fatalities` = D,
`Total Fatalities` = Deaths
) %>%
arrange(Date) %>%
mutate(
Population = comma(round(Population, -3)),
Country = fct_inorder(Country),
Region = str_replace_all(Region, "(Caribbean|Northern America)", "Caribbean & Northern America"),
Region = fct_lump(Region, n = 11)
) %>%
ggplot(aes(`Total Fatalities`, `Daily Fatalities`, colour = Country, label = Date, key = Population)) +
geom_line() +
scale_x_log10(labels = label_comma(accuracy = 1)) +
scale_y_log10(labels = label_comma(accuracy = 0.01), limits = c(0.01, NA)) +
scale_colour_manual(values = countryCols) +
facet_wrap(~Region, ncol = 3, scales = "free_y") +
labs(
x = "Total Fatalities (per million)",
y = "Daily Fatalities (per million)"
)capDailyVsTotal <- glue(
"*Daily Fatalities plotted against Total Fatalities, scaled by population size using Fatalities / Million.
These two values are directly proportional until interventions are successful, at which point the proportional relationship changes, as evidenced by a sudden downwards turn.
Values shown are 7-day simple moving averages in order to minimise the impact of day-to-day variation.
Countries are only shown from the point at which the moving average exceeds {minRate} fatality per million people, and has exceeded this value for > {minDays} days.
Data is additionally restricted to countries with a population > {comma(minPop)}, and those who at one point were recording > {minD} daily fatalities / million.
Importantly, __due to the time taken from the initial infection to the day of death, this is a lag indicator of the control of infection__.*
"
)ggplotly(
dailyVsTotalDeaths
) %>%
shiftAxisLabel(1.6)Daily Fatalities plotted against Total Fatalities, scaled by population size using Fatalities / Million. These two values are directly proportional until interventions are successful, at which point the proportional relationship changes, as evidenced by a sudden downwards turn. Values shown are 7-day simple moving averages in order to minimise the impact of day-to-day variation. Countries are only shown from the point at which the moving average exceeds 1 fatality per million people, and has exceeded this value for > 14 days. Data is additionally restricted to countries with a population > 4,000,000, and those who at one point were recording > 0.45 daily fatalities / million. Importantly, due to the time taken from the initial infection to the day of death, this is a lag indicator of the control of infection.
Comparison of Total Fatalities
nCnt <- 25
topDeaths <- deaths %>%
dplyr::filter(deaths > 0) %>%
group_by(Country, date) %>%
summarise_at(vars(deaths), sum) %>%
dplyr::filter(
deaths == max(deaths),
date == max(date)
) %>%
ungroup() %>%
inner_join(wikiPops) %>%
mutate(
rate = 1e6*deaths / Population,
totalRank = nrow(.) - rank(deaths) + 1,
rateRank = nrow(.) - rank(rate) + 1,
aveRank = (totalRank + rateRank)/2
) %>%
arrange(aveRank, desc(Population)) %>%
dplyr::slice(1:nCnt) %>%
arrange(desc(rateRank)) %>%
mutate(Country = fct_inorder(Country)) %>%
arrange(rateRank) %>%
mutate(Continent = fct_inorder(as.character(Continent)))
a <- topDeaths %>%
ggplot(aes(Country, rate, fill = Country)) +
geom_col(colour = "black") +
geom_label(
aes(y = rate + 50, label = round(rate, 0)),
fill = "white", alpha = 0.5
) +
scale_fill_viridis_d(option = "magma", direction = 1) +
scale_y_continuous(expand = expansion(c(0, 0.08))) +
labs(y = "Fatalities / Million") +
coord_flip() +
facet_grid(Continent~., scales = "free_y", space = "free_y") +
theme(legend.position = "none")
b <- topDeaths %>%
ggplot(aes(Country, deaths, fill = Country)) +
geom_col(colour = "black") +
geom_label(
aes(y = deaths + 10000, label = comma(deaths, accuracy = 1)),
fill = "white", alpha = 0.5
) +
scale_fill_viridis_d(option = "magma", direction = 1) +
scale_y_continuous(expand = expansion(c(0, 0.08)), labels = comma) +
labs(y = "Total Fatalities") +
coord_flip() +
facet_grid(Continent~., scales= "free_y", space = "free_y") +
theme(legend.position = "none")
cp <- glue(
"*Comparison of the {nrow(a$data)} most impacted countries.
Countries were ranked by total fatalities and by fatalities / million, with the {nrow(a$data)} most highly ranked across both methods are shown.
Fatalities are shown using A) the number of fatalities scaled by population size (Fatalities / Million) and B) Total Fatalities.
Countries are also grouped by their continent as designated by the UN classifications, and within each continent each country is ordered by Fatalities / Million.
For fatality rates scaled by population size, it is important to realise that a value of 500 indicates that one in every 2000 people from the total population has died (ignoring demographics).
Similarly, a value of 100 indicates that 1 in every 10,000 from the total population has died in that country.
Whilst the US currently has the most fatalities, 1 in every {comma(round(1e6 / dplyr::filter(a$data, Country == 'US')$rate, 0))} from the US population have currently been confirmed to have died from COVID19.
For {as.character(a$data$Country[which.max(a$data$rate)])}, 1 in every {comma(round(1e6 / max(a$data$rate), 0))} people are recorded as having died from COVID 19.
Importantly, whilst the fatality count in countries like the US and the UK are below the statistical value of 'excess deaths' observed throughout these countries, this is not true for Belgium and the fatality rate for Belgium is more likely to reflect an accurate assessment of the true fatalities due to COVID-19.
In contrast, the values for the US and the UK are likely to be under-estimates of the true fatalities.*"
)plot_grid(
a + theme(legend.position = "none"),
b +
theme(
legend.position = "none",
axis.title.y = element_blank()
),
labels = c("A", "B"),
nrow = 1
)
Comparison of the 25 most impacted countries. Countries were ranked by total fatalities and by fatalities / million, with the 25 most highly ranked across both methods are shown. Fatalities are shown using A) the number of fatalities scaled by population size (Fatalities / Million) and B) Total Fatalities. Countries are also grouped by their continent as designated by the UN classifications, and within each continent each country is ordered by Fatalities / Million. For fatality rates scaled by population size, it is important to realise that a value of 500 indicates that one in every 2000 people from the total population has died (ignoring demographics). Similarly, a value of 100 indicates that 1 in every 10,000 from the total population has died in that country. Whilst the US currently has the most fatalities, 1 in every 2,570 from the US population have currently been confirmed to have died from COVID19. For Belgium, 1 in every 1,183 people are recorded as having died from COVID 19. Importantly, whilst the fatality count in countries like the US and the UK are below the statistical value of ‘excess deaths’ observed throughout these countries, this is not true for Belgium and the fatality rate for Belgium is more likely to reflect an accurate assessment of the true fatalities due to COVID-19. In contrast, the values for the US and the UK are likely to be under-estimates of the true fatalities.
Fatality Rate Vs Time
minDays <- 40
minPop <- 4e6
startingPoint <- 2
df <- confirmed %>%
inner_join(deaths) %>%
group_by(Country, date) %>%
summarise_at(
vars(confirmed, deaths),
sum
) %>%
inner_join(
wikiPops %>% dplyr::filter(Population > minPop | Country %in% alwaysShow)
) %>%
ungroup() %>%
mutate(
`Infection Rate` = 1e6 * confirmed / Population,
f = 1e6 * deaths / Population
) %>%
dplyr::filter(
f > startingPoint
# `Infection Rate` > startingPoint
) %>%
group_by(Country) %>%
mutate(
Days = date - min(date),
n = n()
) %>%
dplyr::filter(
n > minDays,
max(deaths, na.rm = TRUE) > 0
) %>%
mutate(
Rate = deaths / confirmed,
`Fatality Rate` = percent(Rate, accuracy = 0.1),
minusT = date - max(date)
) %>%
ungroup()
plotFr <- mutate(
df,
Country = factor(
Country,
levels = df %>%
dplyr::select(Country, minusT, Rate) %>%
pivot_wider(
id_cols = Country,
names_from = minusT,
values_from = Rate
) %>%
as.data.frame() %>%
column_to_rownames("Country") %>%
dist() %>%
hclust() %>%
as.dendrogram() %>%
labels()
)
) %>%
rename_all(str_to_title) %>%
mutate(`Population (millions)` = round(Population / 1e6, 2)) %>%
ggplot(
aes(
x = Days, y = Country, fill = Rate,
conf = Confirmed,
deaths = Deaths,
date = Date,
label = `Fatality Rate`,
key = `Population (millions)`
)
) +
geom_raster() +
geom_vline(
aes(xintercept = Days + 0.5),
data = . %>%
dplyr::filter(Country == "Australia") %>%
dplyr::filter(Date == max(Date)),
linetype = 3,
size = 1/3,
colour = "grey70"
) +
scale_fill_viridis_c(
option = "magma"
) +
scale_x_continuous(
expand = expansion(0, 0),
labels = abs
) +
scale_y_discrete(expand = expansion(0, 0)) +
labs(
x = glue(
"Days since passing {startingPoint} fatalities/million"
),
y = c(),
fill = "Fatality\nRate"
) +
theme(
panel.grid = element_blank()
)cpFr <- glue(
"*Fatality Rate from confirmed cases after passing {startingPoint} fatalities / million.
Only countries with {minDays} days of data beyond this time-point and a population size >{comma(minPop)} are shown.
Country order is based on clustering using the most recent values.
Countries with no recorded fatalities have been excluded for obvious reasons.
The dashed grey line indicates the time-point Australia is currently at.
A clear trend of an increasing fatality rate with time is evident in many countries (e.g. Spain, France, Italy, UK), however, for some countries (e.g. Singapore) this rate appears relatively stable throughout the majority of days.
The overall Fatality Rate for confirmed cases is currently ({percent(fr, accuracy = 0.1)}).*"
)ggplotly(
plotFr ,
tooltip = c(
"Country", "Date", "Days", "Confirmed", "Deaths",
"Fatality Rate", "Population (millions)"
)
)Fatality Rate from confirmed cases after passing 2 fatalities / million. Only countries with 40 days of data beyond this time-point and a population size >4,000,000 are shown. Country order is based on clustering using the most recent values. Countries with no recorded fatalities have been excluded for obvious reasons. The dashed grey line indicates the time-point Australia is currently at. A clear trend of an increasing fatality rate with time is evident in many countries (e.g. Spain, France, Italy, UK), however, for some countries (e.g. Singapore) this rate appears relatively stable throughout the majority of days. The overall Fatality Rate for confirmed cases is currently (4.8%).
Currently Active Infections
rr <- confirmed %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed), sum, na.rm = TRUE) %>%
left_join(
recovered %>%
group_by(Country, date) %>%
summarise_at(vars(recovered), sum, na.rm = TRUE)
) %>%
dplyr::filter(date == max(date)) %>%
ungroup() %>%
summarise(rr = sum(recovered) / sum(confirmed)) %>%
.[["rr"]]predRR <- confirmed %>%
group_by(Country) %>%
dplyr::filter(
date == max(date)
) %>%
ungroup() %>%
left_join(predRecovered) %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed, recovered), sum, na.rm = TRUE) %>%
ungroup() %>%
summarise(rr = sum(recovered) / sum(confirmed)) %>%
.[["rr"]]Information regarding recovered cases is likely to be the least reliable of reported values as many regions do not report updated numbers for several consecutive days. Additionally many regions do not report recovered cases as the criteria for considering a person to have recovered as currently unclear. Given this:
- The current recovery rate from all confirmed cases is 51.2%. Given the level of under-reporting this may also be highly inaccurate.
- As there is poor and inconsistent reporting of recoveries in many countries, an alternate methodology for estimating recoveries is to use the median recovery time of 21 days to predict recovered cases, subtracting those cases which proved fatal. Under this approach 64% of cases are predicted to be recovered.
- Using reported recoveries, 43.9% of all confirmed infections are considered as ‘active’.
- Using predicted recoveries, this changes to 31.0% of confirmed infections predicted to be currently active.
Reported Active Infections Across All Countries
minDays <- 30
startingPoint <- 4
minPop <- 5e6
df <- confirmed %>%
dplyr::filter(
confirmed > 0,
Country != "China (Other)"
) %>%
left_join(deaths) %>%
group_by(Country, date) %>%
summarise_at(
vars(confirmed, deaths), sum
) %>%
inner_join(
recovered %>%
group_by(Country, date) %>%
summarise_at(vars(recovered), sum)
) %>%
mutate_at(vars(confirmed, deaths, recovered), cummax) %>%
mutate(
active = confirmed - deaths - recovered
) %>%
dplyr::filter(!is.na(active)) %>%
inner_join(
dplyr::filter(wikiPops, Population > minPop | Country %in% alwaysShow)
) %>%
mutate(
rate = 1e6 * active / Population,
pass = cummax(rate > startingPoint)
) %>%
dplyr::filter(pass > 0) %>%
group_by(Country) %>%
mutate(
days = date - min(date)
) %>%
dplyr::filter(max(days) > minDays | Country %in% alwaysShow) %>%
dplyr::filter(max(recovered) > 0) %>%
ungroup() %>%
mutate(
days = as.integer(days),
rate = round(rate, 2)
) %>%
arrange(date) %>%
mutate(
Region = str_replace_all(
Region, "(Southern|Western) Europe", "Southern & Western Europe"
),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
),
Country = fct_inorder(Country),
Region = fct_lump(Region, n = 11)
) %>%
rename_all(str_to_title)
nDays <- max(df$Days)
p2 <- df %>%
ggplot(
aes(
x = Days, y = Rate, colour = Country,
Date = Date, Active = Active,
Confirmed = Confirmed, Recovered = Recovered,
Deaths = Deaths
)
) +
geom_line() +
scale_x_continuous(
expand = expansion(mult = c(0, 0.05)),
) +
scale_y_log10(
expand = expansion(mult = c(0, 0.05)),
labels = comma_format(accuracy = 1),
breaks = 10^seq(1, 4)
) +
scale_colour_manual(values = countryCols) +
xlab(
paste(
"Days since passing",
startingPoint,
"confirmed active cases/million"
)
) +
ylab("Confirmed Active Infection Rate (cases/million)") +
facet_wrap(~Region, ncol = 3)ggplotly(
p2 +
coord_cartesian(ylim = c(1, max(p2$data$Rate)))
) %>%
shiftAxisLabel(1.8)Confirmed active cases of COVID-19 for countries where the confirmed infection rate has exceeded 4 confirmed active cases/million for more than 161 calendar days. Only countries with a population greater than 5,000,000 are shown for better visualisation. Due to difficulties introduced by the currently reported low active infection rate outside Hubei province, data from China has been excluded from this plot, with the exception of Hubei and Hong Kong. Recovered cases are poorly and irregularly reported by many countries (e.g. Ireland, Serbia & Norway). Some countries, such as Sweden, are not reporting recovered cases and these countries have been excluded from this plot. As a result, this plot may indicate multiple instances of a sudden decline which are a simple artefact of data release schedules. To hide a country, click on the country in the plot legend. Clicking again on the country in the legend will restore the data within the plot. Countries are shown in order of the date at which they passed the 4 confirmed active case/million mark. Due to the number of countries shown, you may need to scroll through the legend. Regions of the plot are also able to be zoomed interactively. Please note the y-axis is shown on the logarithmic scale, so that a series of points which appear to be diagonal will indicate exponential growth/decay. Given the different starting point to the previous plot, data will generally be shown for fewer time-points.
Predicted Active Infections Across All Countries
df <- confirmed %>%
dplyr::filter(
confirmed > 0,
Country != "China (Other)"
) %>%
left_join(deaths) %>%
group_by(Country, date) %>%
summarise_at(
vars(confirmed, deaths), sum
) %>%
inner_join(
predRecovered %>%
group_by(Country, date) %>%
summarise_at(vars(recovered), sum)
) %>%
mutate_at(vars(confirmed, deaths, recovered), cummax) %>%
mutate(
active = confirmed - deaths - recovered
) %>%
dplyr::filter(!is.na(active)) %>%
inner_join(
dplyr::filter(wikiPops, Population > minPop | Country %in% alwaysShow)
) %>%
mutate(
rate = 1e6 * active / Population,
pass = cummax(rate > startingPoint)
) %>%
dplyr::filter(pass > 0) %>%
group_by(Country) %>%
mutate(
days = date - min(date)
) %>%
dplyr::filter(max(days) > minDays | Country %in% alwaysShow) %>%
dplyr::filter(max(recovered) > 0) %>%
ungroup() %>%
mutate(
days = as.integer(days),
rate = round(rate, 2)
) %>%
arrange(date) %>%
mutate(
Region = str_replace_all(
Region, "(Southern|Western) Europe", "Southern & Western Europe"
),
Region = str_replace_all(
Region, "(Central|Southern) Asia", "Central & Southern Asia"
),
Region = str_replace_all(
Region, "(Central America|Northern America|Caribbean)", "Caribbean, Central & Northern America"
),
Region = str_replace_all(
Region, "(Eastern|Middle|Southern) Africa", "Eastern, Middle & Southern Africa"
),
Country = fct_inorder(Country),
Region = fct_lump(Region, n = 11)
) %>%
rename_all(str_to_title)
nDays <- max(df$Days)
p2 <- df %>%
ggplot(
aes(
x = Days, y = Rate, colour = Country,
Date = Date, Active = Active,
Confirmed = Confirmed, Recovered = Recovered,
Deaths = Deaths
)
) +
geom_line() +
scale_x_continuous(
expand = expansion(mult = c(0, 0.05)),
) +
scale_y_log10(
expand = expansion(mult = c(0, 0.05)),
labels = comma_format(accuracy = 1),
breaks = 10^seq(1, 4)
) +
scale_colour_manual(values = countryCols) +
xlab(
paste(
"Days since passing",
startingPoint,
"predicted active cases/million"
)
) +
ylab("Predicted Active Infection Rate (cases/million)") +
facet_wrap(~Region, ncol = 3)ggplotly(
p2 +
coord_cartesian(ylim = c(1, max(p2$data$Rate)))
) %>%
shiftAxisLabel(1.8)Predicted active cases of COVID-19 for countries where the active infection rate has exceeded 4 predicted active cases/million for more than 140 calendar days. Predicted recovered cases are obtained using the median recovery time of 21 days, accounting for fatal cases. This allows for assessment of countries where recovered cases are poorly reported. Only countries with a population greater than 5,000,000 are shown for better visualisation. Due to difficulties introduced by the currently reported low active infection rate outside Hubei province, data from China has been excluded from this plot, with the exception of Hubei and Hong Kong. To hide a country, click on the country in the plot legend. Clicking again on the country in the legend will restore the data within the plot. Countries are shown in order of the date at which they passed the 4 predicted active case/million mark. Due to the number of countries shown, you may need to scroll through the legend. Regions of the plot are also able to be zoomed interactively. Please note the y-axis is shown on the logarithmic scale, so that a series of points which appear to be diagonal will indicate exponential growth/decay. Given the different starting point to the previous plot, data will generally be shown for fewer time-points.
Summary of All Rates
minPop <- 8e6
p4 <- confirmed %>%
dplyr::filter(
confirmed > 0
) %>%
left_join(deaths) %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed, deaths), sum) %>%
dplyr::filter(date == max(date)) %>%
left_join(
recovered %>%
group_by(Country, date) %>%
summarise_at(vars(recovered), sum)
) %>%
ungroup() %>%
inner_join(
wikiPops %>% dplyr::filter(Population > minPop | Country %in% alwaysShow)
) %>%
mutate(rate = 1e6 * confirmed / Population) %>%
dplyr::filter(rate > startingPoint) %>%
group_by(Country) %>%
mutate(
active = confirmed - recovered - deaths,
active = 100*active / confirmed,
recovered = 100*recovered / confirmed,
fatalities = 100*deaths / confirmed
) %>%
dplyr::filter(max(recovered) > 1) %>%
ungroup() %>%
dplyr::filter(active < 100) %>%
arrange(active) %>%
mutate(Country = fct_inorder(Country)) %>%
pivot_longer(
cols = c(active, recovered, fatalities),
names_to = "Status",
values_to = "Percentage"
) %>%
mutate(
Status = str_to_title(Status),
Status = factor(
Status,
levels = c("Active", "Recovered", "Fatalities")
),
Percentage = round(Percentage, 2)
) %>%
mutate(confirmed = comma(confirmed)) %>%
rename(Confirmed = confirmed) %>%
ggplot(
aes(
Country, Percentage,
fill = Status, cases = Confirmed
)
) +
geom_col() +
scale_fill_manual(
values = c(
Active = "blue",
Recovered = "green",
Fatalities = "red"
)
) +
scale_y_continuous(expand = expansion(0, 0)) +
coord_flip() +
labs(x = c()) +
theme(
legend.position = "none"
)ggplotly(p4)Fatality, Recovery and Active Infection rates for countries which have exceeded 4 confirmed cases / million, and with a population size > 8,000,000. Countries are ordered by the percentage of cases that remain active. Only countries with a reported recovery rate > 1% are shown
Australian Specific Data
ausPops <- tribble(
~State, ~Population,
"New South Wales", 8117976,
"Victoria", 6629870,
"Queensland", 5115451,
"South Australia", 1756494,
"Western Australia", 2630557,
"Tasmania", 535500,
"Northern Territory", 245562,
"Australian Capital Territory", 428060
)Australian State populations were taken from the ABS Website and were accurate in Sept 2019. The difference with previous estimates used above was within 0.04%, and as such no adjustments were made.
A series of complimentary charts regarding the spread of COVID-19 are available from the ABC website.
- It is now clear that Australia is no longer in an exponential growth phase and they pandemic may be under control in all states except Victoria.
- The June outbreak in Victoria is of a similar size as the initial March outbreak
- Using an estimated population size of 25,203,198, the total percentage of the Australian population confirmed as having been infected currently sits at 0.032%, or one person in every 3,150.
- Notably, the proportion of recovered cases is now >90% in all states except Victoria
- NSW Health made a significant change to their recovered cases on 18, June 2020 when cases not tracked after 6 weeks were classified as recovered
confirmed %>%
dplyr::filter(
Country == "Australia",
date >= sort(unique(date), decreasing = TRUE)[2]
) %>%
bind_rows(
group_by(., date) %>%
summarise(
confirmed = sum(confirmed)
) %>%
mutate(
`Province/State` = "Total"
)
) %>%
group_by(`Province/State`) %>%
mutate(
Increase = c(NA, diff(confirmed)),
`% Increase` = c(NA, diff(confirmed)) / min(confirmed)
) %>%
ungroup() %>%
pivot_wider(
id_cols = `Province/State`,
names_from = date,
values_from = c(confirmed, Increase, `% Increase`)
) %>%
dplyr::select_if(function(x){sum(is.na(x)) <= 1}) %>%
rename(State = `Province/State`) %>%
rename_all(str_remove_all, pattern = "confirmed_") %>%
rename_all(str_remove_all, pattern = "_2020.+") %>%
left_join(
dplyr::select(latestAU, State, confirmed, deaths)
) %>%
mutate_at(
vars(confirmed, deaths),
function(x){
x[is.na(x)] <- sum(x, na.rm = TRUE)
x
}
) %>%
left_join(
auRecTS %>%
dplyr::filter(date == dt) %>%
dplyr::select(State = `Province/State`, recovered) %>%
bind_rows(tibble(State = "Total", recovered = sum(.$recovered)))
) %>%
mutate(
`% Increase` = percent(`% Increase`, accuracy = 0.01),
`Fatality Rate` = percent(deaths / confirmed, accuracy = 0.1),
`Recovery Rate` = percent(recovered / confirmed, accuracy = 0.1),
`Currently Active` = confirmed - recovered - deaths,
) %>%
rename(
Fatalities = deaths,
Recovered = recovered
) %>%
dplyr::select(
State, starts_with("20"), ends_with("Increase"), starts_with("Fatal"), starts_with("Recov"), `Currently Active`
) %>%
pander(
justify = "lrrrrrrrrr",
caption = paste(
"*Confirmed cases, fatalities and recoveries reported by each state at time of preparation.",
"Any states with unchanged, or decreasing confirmed cases may indicate delays with the automated data sources, such as health.gov.au or JHU, or that these states have not yet reported for the day.",
"Please note that some discrepancy with dates may occur due to automated data sources obtained different time zones, such as the USA, the UK and Australia.*"
),
emphasize.strong.rows = nrow(.)
)| State | 2020-07-01 | 2020-07-02 | Increase | % Increase | Fatalities | Fatality Rate | Recovered | Recovery Rate | Currently Active |
|---|---|---|---|---|---|---|---|---|---|
| Australian Capital Territory | 108 | 108 | 0 | 0.00% | 3 | 2.8% | 105 | 97.2% | 0 |
| New South Wales | 3,203 | 3,211 | 8 | 0.25% | 51 | 1.6% | 3,026 | 94.2% | 134 |
| Northern Territory | 30 | 31 | 1 | 3.33% | 0 | 0.0% | 30 | 96.8% | 1 |
| Queensland | 1,067 | 1,067 | 0 | 0.00% | 6 | 0.6% | 1,059 | 99.3% | 2 |
| South Australia | 443 | 443 | 0 | 0.00% | 4 | 0.9% | 436 | 98.4% | 3 |
| Tasmania | 228 | 228 | 0 | 0.00% | 13 | 5.8% | 213 | 94.2% | 0 |
| Victoria | 2,231 | 2,303 | 72 | 3.23% | 20 | 0.9% | 1,866 | 81.0% | 417 |
| Western Australia | 611 | 611 | 0 | 0.00% | 9 | 1.5% | 598 | 97.9% | 4 |
| Total | 7,921 | 8,002 | 81 | 1.02% | 106 | 1.3% | 7,333 | 91.7% | 561 |
Plot of Current Australian Values
ausStatsCap <- "*Current confirmed and recovered cases, along with fatalities for Australia only. Active cases are shown as confirmed cases excluding fatalities and those classed as recovered. Loess curves through all points are shown as continuous lines. Data is only shown from 1^st^ March 2020 as this was the date of the first recorded fatality in Australia. Recovered patient information was also sparse in the early stages of data collection, and as a result estimates of active infections will be a significant underestimate until 6^th^ April. In particular, QLD only began reporting recovered cases on this date. NSW followed a fortnight after this date and as such, only the most recent numbers can be considered as accurate. Below this plot, the same figures can be seen broken down by state.*"ggplotly(
confirmed %>%
dplyr::filter(Country == "Australia", confirmed > 0) %>%
left_join(deaths) %>%
left_join(recovered) %>%
mutate_at(vars(confirmed, deaths, recovered), na_locf) %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed, deaths, recovered), sum) %>%
ungroup() %>%
mutate_at(vars(confirmed, deaths, recovered), cummax) %>%
mutate(active = confirmed - deaths - recovered) %>%
pivot_longer(
cols = c(active, confirmed, deaths, recovered),
names_to = "Status",
values_to = "Total"
) %>%
arrange(Status, date) %>%
dplyr::filter(date > ymd("2020-02-29")) %>%
mutate(
Status = str_to_title(Status),
Status = str_replace_all(Status, "Deaths", "Fatal"),
Status = factor(Status, levels = c("Recovered", "Active", "Fatal"))
) %>%
dplyr::filter(Total > 0) %>%
rename_all(str_to_title) %>%
dplyr::filter(Status != "Confirmed") %>%
ggplot(aes(Date, Total, fill = Status)) +
geom_col() +
geom_line(
data = . %>%
group_by(Date) %>%
summarise(
Total = sum(Total)
) %>%
mutate(Status = "Confirmed"),
colour = "blue"
) +
scale_fill_manual(
values = c(
Active = rgb(0, 0, 0),
Confirmed = rgb(0, 0.3, 0.7),
Fatal = rgb(0.8, 0.2, 0.2),
Recovered = rgb(0.2, 0.7, 0.4)
)
) +
scale_x_date(expand = expansion(c(0, 0.03))) +
scale_y_continuous(expand = expansion(c(0, 0.05))) +
labs("Total Cases")
)Current confirmed and recovered cases, along with fatalities for Australia only. Active cases are shown as confirmed cases excluding fatalities and those classed as recovered. Loess curves through all points are shown as continuous lines. Data is only shown from 1st March 2020 as this was the date of the first recorded fatality in Australia. Recovered patient information was also sparse in the early stages of data collection, and as a result estimates of active infections will be a significant underestimate until 6th April. In particular, QLD only began reporting recovered cases on this date. NSW followed a fortnight after this date and as such, only the most recent numbers can be considered as accurate. Below this plot, the same figures can be seen broken down by state.
ggplotly(
confirmed %>%
dplyr::filter(Country == "Australia") %>%
left_join(deaths) %>%
left_join(recovered) %>%
mutate_at(vars(confirmed, deaths, recovered), na_locf) %>%
rename(State = `Province/State`) %>%
dplyr::filter(
date > ymd("2020-03-19"),
) %>%
arrange(date) %>%
group_by(State) %>%
mutate_at(
vars(confirmed, recovered, deaths), cummax
) %>%
ungroup() %>%
left_join(ausPops) %>%
mutate(active = confirmed - deaths - recovered) %>%
pivot_longer(
cols = c(confirmed, deaths, recovered, active),
names_to = "status",
values_to = "count"
) %>%
dplyr::filter(
count > 0,
!(State %in% c("Queensland", "New South Wales") & status == "recovered" & date < ymd("2020-04-06")),
!(State %in% c("South Australia") & status == "recovered" & date < ymd("2020-04-01")),
!(State %in% c("Tasmania") & status == "recovered" & date < ymd("2020-04-02")),
) %>%
dplyr::filter(status != "confirmed") %>%
mutate(
rate = 1e6*count/Population,
rate = round(rate, 2),
status = str_replace(status, "deaths", "fatal") %>% str_to_title(),
status = factor(status, levels = c("Recovered", "Active", "Fatal"))
) %>%
rename_all(str_to_title) %>%
ggplot(aes(Date, Rate, fill = Status, label = Count)) +
geom_col() +
geom_line(
data = . %>%
group_by(State, Date) %>%
summarise(
Rate = sum(Rate),
Count = sum(Count)
) %>%
mutate(Status = "Confirmed"),
colour = "blue"
) +
facet_wrap(~State, ncol = 4) +
scale_fill_manual(
values = c(
Active = rgb(0, 0, 0),
Confirmed = rgb(0, 0.3, 0.7),
Fatal = rgb(0.8, 0.2, 0.2),
Recovered = rgb(0.2, 0.7, 0.4)
)
) +
scale_x_date(expand = expansion(c(0, 0.03))) +
labs(y = "Rate (Cases / Million)")
)Breakdown of individual states. Victorian recovered numbers began to be accurately reported from 22nd March, with other states gradually providing this information. NSW/QLD recovered cases have only recently begun being reported and up until the most recent dates, recovered/active values were very approximate for these states. The extreme drop for NSW active cases in early June is a function of the changed reporting strategy implemented by NSW Health.
Daily New Cases
ggplotly(
confirmed %>%
dplyr::filter(Country == "Australia") %>%
group_by(`Province/State`) %>%
mutate(daily = c(0, diff(confirmed))) %>%
ungroup() %>%
dplyr::filter(confirmed > 0) %>%
mutate(
daily = case_when(
daily < 0 ~ 0,
daily >= 0 ~ daily
)
) %>%
bind_rows(
group_by(., date) %>%
summarise(daily = sum(daily)) %>%
ungroup() %>%
mutate(`Province/State` = "All States")
) %>%
group_by(`Province/State`) %>%
mutate(
MA = round(sma(daily, 7), 2),
`Above Average` = daily > MA
) %>%
dplyr::filter(date > "2020-03-01") %>%
ggplot(aes(date, daily)) +
geom_col(
aes(fill = `Above Average`, colour = `Above Average`),
data = . %>% dplyr::filter(!is.na(`Above Average`)),
# fill = "grey80",
# colour = "grey50",
width = 1/2
) +
geom_line(aes(y = MA), colour = "blue") +
facet_wrap(~`Province/State`, scales = "free_y") +
labs(
x = "Date",
y = "Daily New Cases",
fill = "\nAbove\nAverage"
) +
scale_fill_manual(values = c("white", rgb(1, 0.2, 0.2))) +
scale_colour_manual(values = c("grey50", rgb(1, 0.2, 0.2))),
tooltip = c(
"date", "daily", "MA"
)
)Daily new cases for each state shown against the 7-day average. Days which are above average are highlighted in red.
Current Growth Factor
n <- 14
cp <- glue(
"*Growth factor for each State/Territory.
This value becomes volatile when daily new cases approach zero as is commonly observed in small populations, and at the end stages of an outbreak.
In order to try and minimise volatility a {n} day simple moving average was used, in contrast to the 5 day average as advocated [here](https://www.abc.net.au/news/2020-04-10/coronavirus-data-australia-growth-factor-covid-19/12132478).
This enables assessment of the growth factor over an entire quarantine period.
If no new cases are observed over this period, the value is not able to be calculated.
The dashed vertical lines indicate the day most state borders were closed.*"
)gf <- list(
confirmed %>%
dplyr::filter(Country == "Australia") %>% #, date < dt) %>%
arrange(date) %>%
group_by(
`Province/State`
) %>%
mutate(
new = c(0, diff(confirmed)),
new_ma = sma(new, n)
) %>%
dplyr::filter(confirmed > 0, !is.na(new_ma)) %>%
mutate(
R = c(NA, new_ma[-1] / new_ma[-n()]),
R = case_when(
is.nan(R) ~ NA_real_,
!is.nan(R) ~ R
)
) %>%
ungroup() %>%
arrange(`Province/State`),
confirmed %>%
dplyr::filter(Country == "Australia") %>% #, date < dt) %>%
arrange(date) %>%
group_by(Country, date) %>%
summarise_at(vars(confirmed), sum) %>%
ungroup() %>%
mutate(
new = c(0, diff(confirmed)),
new_ma = sma(new, n)
) %>%
dplyr::filter(confirmed > 0, !is.na(new_ma)) %>%
mutate(
R = c(NA, new_ma[-1] / new_ma[-n()]),
R = case_when(
is.nan(R) ~ NA_real_,
!is.nan(R) ~ R
),
`Province/State` = "All States"
) %>%
arrange(`Province/State`)
) %>%
bind_rows() %>%
dplyr::filter(date > ymd("2020-03-15")) %>%
ggplot(aes(date, R, colour = `Province/State`)) +
geom_ribbon(aes(ymin = 1, ymax = R), alpha = 0.1) +
geom_hline(yintercept = 1) +
geom_vline(
xintercept = ymd("2020-03-22"),
linetype = 2,
colour = "grey30"
) +
geom_label(
aes(label = R),
data = . %>%
dplyr::filter(date == max(date)) %>%
mutate(R = round(R, 2), date = date + 1),
fill = rgb(1, 1, 1, 0.3),
show.legend = FALSE
) +
labs(
x = "Date", y = "Growth Factor"
) +
facet_wrap(~`Province/State`, scales = "free_x") +
theme(legend.position = "none") +
coord_cartesian(ylim = c(0.0, 3))
gf.
This enables assessment of the growth factor over an entire quarantine period.
If no new cases are observed over this period, the value is not able to be calculated.
The dashed vertical lines indicate the day most state borders were closed.*](data:image/png;base64,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Growth factor for each State/Territory. This value becomes volatile when daily new cases approach zero as is commonly observed in small populations, and at the end stages of an outbreak. In order to try and minimise volatility a 14 day simple moving average was used, in contrast to the 5 day average as advocated here. This enables assessment of the growth factor over an entire quarantine period. If no new cases are observed over this period, the value is not able to be calculated. The dashed vertical lines indicate the day most state borders were closed.
The current 14 day growth factor is 1.1 which gives considerable cause for concern. In particular, the outbreak in Victoria is clearly not under control.
Testing Within Each State
latestAU %>%
dplyr::select(State, Tested = tested) %>%
write_tsv(
glue(
"tested/tested_{format(dt, '%Y%m%d')}.tsv"
)
)Testing numbers were initially sourced from manual inspection of individual press releases for NSW, QLD, VIC, WA, SA, TAS, ACT and the NT. Updates on testing numbers beyond the initial values were performed automatically using the above code which probes each state’s official releases for the latest values, and updates where found. The number of tested individuals in each state was then assessed as a function of State population size. All results are valid at the time of report generation.
latestAU %>%
dplyr::select(State, Tested = tested) %>%
full_join(
confirmed %>%
dplyr::filter(
date == max(date),
Country == "Australia"
) %>%
rename(
State = `Province/State`
)
) %>%
mutate(
Tested = case_when(
is.na(Tested) ~ confirmed,
Tested < confirmed ~ confirmed,
!is.na(Tested) ~ Tested
)
) %>%
left_join(ausPops) %>%
bind_rows(
tibble(
State = "Total",
Population = sum(.$Population, na.rm = TRUE),
confirmed = sum(.$confirmed, na.rm = TRUE),
Tested = sum(.$Tested, na.rm = TRUE)
)
) %>%
mutate(
`Tests / '000` = round(1e3 * Tested / Population, 2),
Positive = confirmed / Tested,
Negative = 1 - Positive,
isTotal = grepl("Total", State)
) %>%
dplyr::select(
State, Population,
Confirmed = confirmed,
Tested,
contains("000"),
ends_with("ive"),
isTotal
) %>%
arrange(isTotal, desc(`Tests / '000`)) %>%
dplyr::select(-isTotal) %>%
dplyr::rename(
`% Positive Tests` = Positive,
`% Negative Tests` = Negative
) %>%
mutate_at(
vars(starts_with("%")), percent, accuracy = 0.01
) %>%
pander(
justify = "lrrrrrr",
missing = "",
caption = glue(
"*COVID-19 testing scaled by state population size.
Confirmed cases are assumed to be the tests returning a positive result.
The current numbers available for some states are a lower limit, and as such, the proportion of the population tested is likely to be higher, as is the proportion of tests returning a negative result.*"
),
emphasize.strong.rows = nrow(.)
)| State | Population | Confirmed | Tested | Tests / ’000 | % Positive Tests | % Negative Tests |
|---|---|---|---|---|---|---|
| Victoria | 6,629,870 | 2,303 | 850,000 | 128.2 | 0.27% | 99.73% |
| New South Wales | 8,117,976 | 3,211 | 889,914 | 109.6 | 0.36% | 99.64% |
| Tasmania | 535,500 | 228 | 50,606 | 94.5 | 0.45% | 99.55% |
| South Australia | 1,756,494 | 443 | 155,483 | 88.52 | 0.28% | 99.72% |
| Queensland | 5,115,451 | 1,067 | 376,719 | 73.64 | 0.28% | 99.72% |
| Australian Capital Territory | 428,060 | 108 | 31,197 | 72.88 | 0.35% | 99.65% |
| Western Australia | 2,630,557 | 611 | 188,953 | 71.83 | 0.32% | 99.68% |
| Northern Territory | 245,562 | 31 | 13,604 | 55.4 | 0.23% | 99.77% |
| Total | 25,459,470 | 8,002 | 2,556,476 | 100.4 | 0.31% | 99.69% |
R Session Information
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=C, LC_NUMERIC=C, LC_TIME=C, LC_COLLATE=C, LC_MONETARY=C, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: readxl(v.1.3.1), ggfortify(v.0.4.10), QuantTools(v.0.5.7), data.table(v.1.12.8), cowplot(v.1.0.0), plotly(v.4.9.2.1), DT(v.0.14), pander(v.0.6.3), RCurl(v.1.98-1.2), rvest(v.0.3.5), xml2(v.1.3.2), jsonlite(v.1.7.0), glue(v.1.4.1), broom(v.0.5.6), ggrepel(v.0.8.2), matrixStats(v.0.56.0), scales(v.1.1.1), lubridate(v.1.7.9), magrittr(v.1.5), forcats(v.0.5.0), stringr(v.1.4.0), dplyr(v.1.0.0), purrr(v.0.3.4), readr(v.1.3.1), tidyr(v.1.1.0), tibble(v.3.0.1), ggplot2(v.3.3.2) and tidyverse(v.1.3.0)
loaded via a namespace (and not attached): httr(v.1.4.1), viridisLite(v.0.3.0), here(v.0.1), modelr(v.0.1.8), fasttime(v.1.0-2), assertthat(v.0.2.1), highr(v.0.8), selectr(v.0.4-2), blob(v.1.2.1), cellranger(v.1.1.0), yaml(v.2.2.1), pillar(v.1.4.4), backports(v.1.1.8), lattice(v.0.20-41), digest(v.0.6.25), colorspace(v.1.4-1), htmltools(v.0.5.0), pkgconfig(v.2.0.3), haven(v.2.3.1), generics(v.0.0.2), farver(v.2.0.3), ellipsis(v.0.3.1), withr(v.2.2.0), lazyeval(v.0.2.2), cli(v.2.0.2), crayon(v.1.3.4), evaluate(v.0.14), fs(v.1.4.1), fansi(v.0.4.1), nlme(v.3.1-148), Cairo(v.1.5-12), tools(v.3.6.3), hms(v.0.5.3), lifecycle(v.0.2.0), munsell(v.0.5.0), reprex(v.0.3.0), compiler(v.3.6.3), rlang(v.0.4.6), grid(v.3.6.3), rstudioapi(v.0.11), htmlwidgets(v.1.5.1), crosstalk(v.1.1.0.1), labeling(v.0.3), bitops(v.1.0-6), rmarkdown(v.2.3), gtable(v.0.3.0), DBI(v.1.1.0), curl(v.4.3), R6(v.2.4.1), gridExtra(v.2.3), knitr(v.1.29), rprojroot(v.1.3-2), stringi(v.1.4.6), Rcpp(v.1.0.4.6), vctrs(v.0.3.1), dbplyr(v.1.4.4), tidyselect(v.1.1.0) and xfun(v.0.15)